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The inspection of conductive particles is a crucial step in the Thin Film Transistor Liquid Crystal Display (TFT-LCD) circuit detection process since only high-quality deformed particles have the conductive effect in the circuit. The main task of detecting conduction particles is to locate and count the valid particles accurately, which is a high challenge due to various difficulties such as the uneven illumination, different sizes to aggregation and overlap between particles, etc. Traditional detection algorithms need to manually set a large number of artificial thresholds, which limits their adaptability. As a result, effective automatic detection of conductive particles is strongly motived in industry. In this paper, a novel particle detection algorithm based on generative adversarial networks (GAN) is proposed for TFT-LCD circuit inspection system. The backbone architecture of the generator is based on a compact end-to-end neural network with multi-scale convolution blocks for well utilizing the multiscale spatial features. And the discriminator is designed to detect and correct high-order inconsistencies for real-fake images. Moreover, Coarse to Fine training strategy and Loss functions Coordination strategy are further proposed to improve the detection quality. The experiments on the real dataset demonstrate the effectiveness of the proposed methods for the detection of valid conductive particles compared to the state-of-the-art methods.

Detection of Conductive Particles in TFT-LCD Circuit Using Generative Adversarial Networks